Pattern recognition adaptive controller

ABSTRACT

A pattern recognition adaptive controller configured to dynamically adjust proportional gain and integral time control parameters based upon patterns that characterize the closed-loop response. The pattern recognition adaptive controller receives a sampled signal representative of the controlled variable, and determines a smoothed signal based on the sampled signal. The controller determines an estimated noise level of the sampled signal and determines if the control output and process output are oscillating quickly based on predefined criteria. The controller adjusts the gain used by the controller if the control output and process output are oscillating quickly. If the control output and process are not oscillating quickly, the controller determines whether there has been a significant load disturbance, whether there is an insignificant pattern, and/or whether the control output is saturated. Based on the results of these determinations, the controller either leaves the gain and integral time unchanged or determines new gain and integral time. The adjusted control parameters are then used to control the actuator, thereby causing the controller to affect the process.

FIELD OF THE INVENTION

The present invention relates to an apparatus and method for adjustingthe gain and integral time parameters of a proportional-integralcontroller. More specifically, the present invention relates to anapparatus and method for adjusting the gain and integral time parametersof a proportional-integral controller in response to patterns in afeedback signal representative of a controlled variable.

BACKGROUND OF THE INVENTION

Single-loop feedback controllers (“controllers”) are commonly employedto maintain temperature, humidity, pressure, and flow rates for heating,ventilating, and air-conditioning equipment. For example, in an airconditioning system, a controller may be used to control the flow ofchilled water through a cooling coil. In such a system, the controlleradjusts the water flow rate based on a feedback signal indicative of thetemperature of the air discharged from the coil (the “controlledvariable”). The feedback signal is generated by a sensor disposed tomonitor the controlled variable.

The object of such controllers is to control the system in such a way asto maintain the controlled variable, as sensed by the feedback signal,at a desired level (the “setpoint”). For example, the controller of anair conditioning system attempts to maintain the temperature of the airdischarged from the system at a specific level. When the actualtemperature of the discharged air deviates from the desired temperature,the controller must appropriately adjust the flow of the chilled waterto bring the actual air temperature back in line with the desired airtemperature. Thus, if the feedback signal indicates that the actual airtemperature is colder than the desired temperature, the controller willcause the flow rate of chilled water to decrease, which will cause theactual temperature of the discharged air to increase. Likewise, if thefeedback signal indicates that the actual air temperature is warmer thanthe desired temperature, the controller will cause the flow rate ofchilled water to increase, which will cause the actual temperature ofthe discharged air to decrease.

An ideal feedback control system would be able to maintain thecontrolled variable at the setpoint based only on the feedback signal.However, actual feedback control systems require additional inputs knownas control parameters. Control parameters are values used by acontroller to determine how to control a system based on the feedbacksignal and the setpoint.

One commonly used method for controlling a closed loop system, known asproportional plus integral control (PI), requires two controlparameters: proportional gain and integral time. Since these two controlparameters directly affect the performance and stability of a PIcontroller, it is important to determine the appropriate values of theseparameters. However, the appropriate values for these parameters maychange over time as the system is used. For example, the dynamics of aprocess may be altered by heat exchanger fouling, inherent nonlinearbehavior, ambient variations, flow rate changes, large and frequentdisturbances, and unusual operations status, such as failures, startupand shutdown. The process of adjusting the control parameters of acontroller to compensate for such system changes is called retuning. Ifa controller is not retuned, the control response may be poor. Forexample, the controlled variable may become unstable or oscillate widelywith respect to the setpoint. Thus, to insure adequate performance,controllers should be periodically retuned with new control parametervalues.

The various tuning methods which have been developed to determine theappropriate values of the control parameters for PI controllers fallinto three general categories. These categories are: manual tuning,auto-tuning, and adaptive control. Manual tuning methods require anoperator to run different test or trial and error procedures todetermine the appropriate control parameters. Manual tuning methods havethe obvious disadvantage of requiring large amounts of operator time andexpertise. Auto-tuning methods require an operator to periodicallyinitiate tuning procedures, during which the controller willautomatically determine the appropriate control parameters. The controlparameters thus set will remain unchanged until the next tuningprocedure. While auto-tuning requires less operator time than manualtuning methods, it still requires operator intervention. Further, duringthe interval between tunings, the controller may become severely out oftune and operate poorly. With adaptive control methods, the controlparameters are automatically adjusted during normal operation to adaptto changes in process dynamics. Thus, no operator intervention isrequired. Further, the control parameters are continuously updated toprevent the degraded performance which may occur between the tunings ofthe other methods.

There are three main approaches to adaptive control: model referenceadaptive control (“MRAC”), self-tuning control, and pattern recognitionadaptive control (“PRAC”). The first two approaches, MRAC andself-tuning, rely on system models which are generally quite complex.The complexity of the models is necessitated by the need to anticipateunusual or abnormal operating conditions. Specifically, MRAC involvesadjusting the control parameters until the response of the system to acommand signal follows the response of a reference model. Self-tuningcontrol involves determining the parameters of a process model on-lineand adjusting the control parameters based upon the parameters of theprocess model.

With PRAC, parameters that characterize the pattern of the closed-loopresponse are determined after significant setpoint changes or loaddisturbances have occurred. The control parameters are then adjustedbased upon the characteristic parameters of the closed-loop response.Some known pattern recognition adaptive controllers require an operatorto enter numerous control parameters before normal operation may begin.The more numerous the operator selected control parameters, the moredifficult it is to adjust the pattern recognition adaptive controllerfor optimal performance, and the longer it takes to prepare the patternrecognition adaptive controller for operation.

Significant advances in the art have been disclosed in commonly ownedU.S. Pat. Nos. 5,355,305 and 5,506,768, the entire contents of which areincorporated herein by reference. U.S. Pat. Nos. 5,355,305 and 5,506,768(the '305 and '768 patents) provide for a pattern recognition adaptivecontroller with fewer operator-specified control variables than arerequired by other known pattern recognition adaptive controllers. The'305 and '768 patents further provide for a pattern recognition adaptivecontroller with improved performance, and particularly one whichperforms in a near-optimal manner under a large amount of noise. The'305 and '768 patents further provide for a pattern recognition adaptivecontroller with a variable tune noise band which adjusts automaticallyto different noise levels in the process. The '305 and '768 patents alsoprovide for a pattern recognition adaptive controller which efficientlycontrols a process with a reduced number of actuator adjustments, andtherefore reduced energy costs, by decreasing oscillations for thecontrolled variable signal. The '305 and '768 patents further providefor a robust pattern recognition adaptive controller that performsrelatively secure control without constraining the values of itsparameters to a predetermined range. The '305 and '768 patents alsoprovide for a pattern recognition adaptive controller with reducedresource requirements, and more particularly, which requires less memoryand less computational power than previous pattern recognition adaptivecontrollers.

While the inventions disclosed in U.S. Pat. Nos. 5,355,305 and 5,506,768represent significant advances in the art, it is desirable to provide afurther improved method for automatically adjusting the gain andintegral time of proportional-integral controllers based upon patternsthat characterize the closed-loop response. In particular, it isdesirable to provide a pattern recognition adaptive controller that doesnot detune when there are periodic load disturbances. It is alsodesirable to provide a pattern recognition adaptive controller that doesnot detune when the controller gain is extremely large compared to anoptimal value. Finally, it is desirable to provide a pattern recognitionadaptive controller that does not detune undersized systems that arestarted repeatedly.

SUMMARY OF THE INVENTION

The present invention provides an improved apparatus and method foradjusting the gain and integral time parameters of a PI controller inresponse to patterns in a feedback signal representative of a controlledvariable. An exemplary embodiment of the present invention provides amethod of dynamically adjusting the control parameters of a proportionalgain and integral time controller disposed to control an actuatoraffecting a process. The method comprises sampling a feedback signalrepresentative of a controlled variable of the process to generate asampled signal, generating a smoothed signal based on the sampledsignal, determining an estimated noise level of the sampled signal,determining if control output and process output are oscillating quicklybased on predefined criteria, adjusting the gain used by the controllerif the control output and process output are oscillating quickly, andutilizing the adjusted control parameters to control the actuator,thereby causing the controller to affect the process.

Another embodiment of the present invention provides an apparatus fordynamically adjusting control parameters of a proportional gain andintegral time controller disposed to control an actuator affecting aprocess. The apparatus comprises means for sampling a feedback signalrepresentative of a controlled variable of the process to generate asampled signal, means for generating a smoothed signal based on thesampled signal, means for determining an estimated noise level of thesampled signal, means for determining if control output and processoutput are oscillating quickly based on predefined criteria, means foradjusting the gain used by the controller if the control output andprocess output are oscillating quickly, and means for utilizing theadjusted control parameters to control the actuator, thereby causing thecontroller to affect the process.

Another embodiment of the present invention provides a method ofdynamically adjusting the control parameters of a proportional gain andintegral time controller disposed to control an actuator affecting aprocess. The method comprises sampling a feedback signal representativeof a controlled variable of the process to generate a sampled signal,generating a smoothed signal based on the sampled signal, anddetermining an estimated noise level of the sampled signal. In addition,the method comprises determining whether a pattern is insignificantbased on a setpoint and tune band and whether the control output issaturated, determining a new gain and a new integral time and settingthe gain and integral time of the controller to the new gain and newintegral time if the pattern is not insignificant and the control outputis not saturated, and utilizing the adjusted control parameters tocontrol the actuator, thereby causing the controller to affect theprocess.

The present invention further relates to various features andcombinations of features shown and described in the disclosedembodiments. Other ways in which the objects and features of thedisclosed embodiments are accomplished will be described in thefollowing specification or will become apparent to those skilled in theart after they have read this specification. Such other ways are deemedto fall within the scope of the disclosed embodiments if they fallwithin the scope of the claims which follow.

DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram illustrating the components of a closed loopfeedback system according to an exemplary embodiment.

FIG. 2A is a block diagram illustrating a pattern recognition adaptivecontroller according to a preferred embodiment.

FIG. 2B is a block diagram illustrating a pattern recognition adaptivecontroller according to alternative embodiment.

FIG. 3 is a flow diagram illustrating the manner in which the controllerof FIG. 1 may be implemented for dynamically adjusting controlparameters according to an exemplary embodiment of the presentinvention.

FIGS. 4A, 4B and 4C show signals used to determine features for setpointchanges with overshoot in accordance with the flow diagram of FIG. 3.

FIGS. 5A, 5B and 5C show signals used to determine features for loaddisturbances in accordance with the flow diagram of FIG. 3.

DETAILED DESCRIPTION

Before explaining a number of preferred, exemplary, and alternativeembodiments of the invention in detail it is to be understood that theinvention is not limited to the details of configuration and thearrangement of the components set forth in the following description orillustrated in the drawings. The invention is capable of otherembodiments or being practiced or carried out in various ways. It isalso to be understood that the phraseology and terminology employedherein is for the purpose of description and should not be regarded aslimiting.

FIG. 1 shows the hardware configuration of a closed-loop PI controlsystem 10 embodying the present invention. System 10 generally includesa PI controller 20, an actuator 28, a subsystem 32 which controls aprocess, and a sensor 36. Controller 20 is coupled to actuator 28through a digital to analog converter 24, and to sensor 36 through ananalog to digital converter 40.

Actuator 28 is disposed to affect the operation of subsystem 32. Forexample, subsystem 32 may be an air conditioning subsystem for whichactuator 28 controls a valve through which chilled water passes. Sensor36 is disposed to monitor the controlled variable of subsystem 32, whichis affected by actuator 28. For example, sensor 36 may be a thermometerdisposed to monitor the temperature of air that is discharged fromsubsystem 32. Sensor 36 transmits a signal representative of thecontrolled variable (temperature) to analog to digital converter 40.This controlled variable signal is preferably filtered by ananti-aliasing filter to remove high frequency signals as understood tothose skilled in the art. Analog to digital converter 40 samples thefiltered controlled variable signal and transmits a sampled feedbacksignal to controller 20. Controller 20 compares the sampled feedbacksignal to a setpoint 46, which is representative of the desired value ofthe controlled variable, to determine the extent to which the controlledvariable has diverged from setpoint 46. Such divergences may be causedby setpoint changes or load disturbances. Based on that comparison,controller 20 determines how actuator 28 should respond to cause thecontrolled variable to return to setpoint 46. Once the appropriateresponse is determined, controller 20 generates a control signal throughdigital to analog converter 24 to actuator 28. According to alternativeembodiments, instead of using digital to analog converter 24, any othertype of output device may be used. For example, a pulse modulationadaptive controller may be substituted in place of digital to analogconverter 24. In response to the control signal, actuator 28appropriately alters the operation of subsystem 32. During thisprocedure, the control parameters of controller 20 are retuned tocompensate for any changes in the process. Preferably, the new PI valuesare chosen to minimize the integrated absolute errors between setpoint46 and the controlled variable.

A crucial factor in the efficiency and performance of system 10 is theaccuracy with which controller 20 determines the new PI values after anygiven disturbance. A pattern recognition adaptive controller implementedin accordance with the present invention makes this determination bycharacterizing the closed loop response, as the response is reflected inthe sampled feedback signal.

FIG. 2A shows pattern recognition adaptive controller 20 according to apreferred embodiment of the present invention. According to thisembodiment, controller 20 internally incorporates the hardware andsoftware required to implement the pattern recognition adaptive controlprocess. The hardware may include a microprocessor 42 and memory 48.Microprocessor 42 includes an adder 44 and a comparator 46 and operatesaccording to program instructions stored in memory 48. Memory 48 may beROM, EPROM, EEPROM, RAM, or flash loaded with the appropriateinstructions, or any other digital information storage means.

FIG. 2B shows an alternative embodiment of the present invention.According to this embodiment, the process of determining optimal controlparameter values is implemented by an external processing unit 62, suchas a personal computer. The processing unit 62 is connected to a PIcontroller 60 through an interface 64, such as a serial port. Theprocessing unit 62 receives the control signal generated by controller60 via a line 66 and the feedback signal from sensor 36 via a line 68.Based on these signals, processing unit 62 determines the optimalcontrol parameters for controller 60. These parameters are thentransmitted to controller 60 through interface 64. External processingunit 62 may be connected to controller 60 to provide continuousparameter retuning, or may be connected thereto from time to time toprovide retuning on a periodic basis. When processing unit 62 is notconnected to controller 60, the operating parameters of controller 60remain constant at the values generated by processing unit 62 during themost recent retuning operation. A more detailed description of apreferred embodiment of the present invention will now be made withreference to FIG. 3.

FIG. 3 is a block diagram for a system 100 (known as “PRAC+”) inaccordance with a preferred embodiment of the present invention. Theimplementation of system 100 generally comprises block 101 for smoothingthe sampled feedback signal and estimating a noise level, block 103 fordetermining if the control output and process output are oscillatingquickly, block 105 for determining whether a significant loaddisturbance has occurred, block 107 for characterizing a closed loopresponse, block 109 for determining whether a pattern is insignificantand whether the actuator is saturated, block 111 for determining a newgain and new integral time, and block 104 for decreasing gain.

In block 101, a smoothed signal is estimated from the sampled feedbacksignal 102 provided by A/D converter 40. The smoothed process output isdetermined from: $\begin{matrix}{{\hat{y}}_{t - 2} = {\frac{1}{70}\left( {{{- 6}y_{t}} + {24y_{t - 1}} + {34y_{t - 2}} + {24y_{t - 3}} - {6y_{t - 4}}} \right)}} & (1)\end{matrix}$where ŷ_(t-2) is the estimate of the process output at time t-2, y_(t)is the process output at time t, y_(t-1) is the process output one timeperiod prior to time t, y_(t-2) is the process output two time periodsprior to time t, y_(t-3) is the process output three time periods priorto time t, and y_(t-4) is the process output four time periods prior totime t. Equation (1) is based on fitting a quadratic function throughfive evenly spaced points. The smoothed estimate of the slope ŝ_(t-3) ofa signal three time periods prior to time t is determined with:$\begin{matrix}{{\hat{s}}_{t - 3} = {\frac{1}{28T}\left( {{3y_{t}} + {2y_{t - 1}} + y_{t - 2} - y_{t - 4} - {2y_{t - 5}} - {3y_{t - 6}}} \right)}} & (2)\end{matrix}$where T is the sampling period for PI Controller, y_(t-5) is the processoutput five time periods prior to time t, and y_(t-6) is the processoutput six time periods prior to time t. Equation (2) is based uponfitting a quadratic function through seven points. Block 101 uses anestimate of process noise to determine a tune band and a signal-to-noiseratio. The estimate of the process noise at time t is determined fromthe exponential weighted moving average (“EWMA”){overscore (n)} _(t) ={overscore (n)} _(t-1)+λ(|ŷ _(t) −y _(t)|−{overscore (n)} _(t-1))  (3)where {overscore (n)}_(t-1) is the previous noise estimate and λ is asmoothing constant that equals 0.001. A convenient method for startingup the EWMA is to recursively compute the average {overscore (n)}_(t)for the first 1/λ samples with: $\begin{matrix}{{\overset{\_}{n}}_{t} = {{\overset{\_}{n}}_{t - 1} + {\frac{1}{r}\left( {{{{\hat{y}}_{t} - y_{t}}} - {\overset{\_}{n}}_{t - 1}} \right)}}} & (4)\end{matrix}$where r is a running index on the number of sampling periods used in thenoise estimate.

In block 103, a determination is made whether the control output andprocess output are oscillating quickly. Large and quick oscillations inthe control output and process output can be caused by excessive valuesfor the controller gain. To determine if the control output and processoutput are oscillating quickly, block 103 determines the followingfeatures (e.g., predefined criteria) from 12 samples of the error andcontrol output: number of extremes for control signal and error, maximumvalue of low extremes for control signal and error, and minimum value ofhigh extremes for the control signal and error. The error e equals thedifference between the setpoint and process output and is determinedfrom:e=y _(set) −y  (5)where y_(set) is the setpoint. The following rules are used to identifyextremes in the error:IF e_(t-1)>e_(t) and e_(t-1)≧e_(t-2) THEN e_(t-1) is high extremeIF e_(t-1)<e_(t) and e_(t-1)≦e_(t-2) THEN e_(t-1) is low extreme  (6)where e_(t) is the error at time t, e_(t-1) is the error at one samplingperiod prior to time t, and e_(t-2) is the error at two sampling periodsprior to time t. Similar rules are used to determine extremes in thecontrol signal.

Block 103 uses the following rule to determine if the error and controlsignal have fast oscillations:IF [n _(extreme,error)≧4 and n _(extreme,u)≧4 andmax{e _(low,1) , e _(low,2), . . . }<0 and min{e _(high,1), e_(high,2),. . . }>0 and(min{u _(high,1) , u _(high,2), . . . }−max{u _(low,1) , u _(low,2), . .. })>0.1(u _(max) −u_(min))]THEN fast oscillations in error & control signal  (7)where n_(extreme,error) is the number of extremes in the error,n_(extreme,u) is the number of extremes in the control signal, e_(low,1)is the first low extreme in the error, e_(low,2) is the second lowextreme in the error, e_(high,1) is the first high extreme in the error,u_(high,1) is the first high extreme in the control signal, u_(high,2)is the second high extreme in the control signal, u_(low,1) is the firstlow extreme in the control signal, u_(low,2) is the second low extremein the control signal, u_(max) is the maximum value of the controlsignal, and u_(min) is the minimum value of the control signal.

If the process output and control signal are oscillating quickly asdetermined from rule (7), then the ratio of new controller gain(K_(new)) to present controller gain (K) is determined from:$\begin{matrix}{\frac{K_{new}}{K} = {1 - {0.5\left( \frac{{\min\quad\left\{ {u_{{high},1},u_{{high},2},\ldots} \right\}} - {\max\left\{ {u_{{low},1},u_{{low},2},\ldots} \right\}}}{u_{\max} - u_{\min}} \right)}}} & (8)\end{matrix}$

At times, random noise can cause a fast oscillation to be incorrectlyidentified. To slow down the adjustment of gain in the presence ofrandom noise, the 11 historical values for the error are set to zeroafter fast oscillations in the process output and control signal areidentified. If the control output and process output are not oscillatingquickly, system 100 advances to block 105. If the control output andprocess output are oscillating quickly, a new gain value is determinedat block 104 according to equation (8) above.

At block 105, system 100 characterizes the closed loop response after alarge setpoint change or load disturbance has occurred. A setpointchange greater than the tune band is considered large. If the patternfor a setpoint change is not being characterized, then block 105searches for large load disturbances. A load disturbance is consideredlarge when the smoothed process output is greater than the setpoint plustune band for two consecutive samples, or the smoothed process output isless than the setpoint minus tune band for two consecutive samples. Thetune band T_(band) is determined from:T_(band)=max{5.33{overscore (n)}_(t), T_(band,min)}  (9)where {overscore (n)}_(t) is the average noise level as determined fromequations (3) and (4), and T_(band,min) is a minimum tune band. Theconstant 5.33 was determined from optimizations that were designed tominimize the integrated absolute value of the error for a wide range ofsystems as is known in the art. The minimum tune band T_(band,min) canbe estimated with:T_(band,min)=max {4P_(range)R_(D-A), 4S_(range)R_(A-D)}  (10)where P_(range) is the maximum expected range of the process output,R_(D-A) is the resolution of the digital to analog converter, S_(range)is the sensor range, and R_(A-D) is the resolution of the analog todigital converter. The minimum tune band prevents system 100 from tuninglimit-cycle oscillations caused by quantization. If no large setpointchange or load disturbance occurs, the gain and integral time remain thesame. If there is a large setpoint change or load disturbance, system100 advances to block 107.

At block 107, system 100 characterizes the closed loop response bydetermining the following seven features from the control signal,smoothed process output and slope of the process output: low controlleroutput (u_(low)), high controller output (u_(high)), low smoothedprocess output (ŷ_(low)), high smoothed process output (ŷ_(high)),oscillation ratio (φ), closed loop response time (θ), and an indicationof overshoot. The oscillation ratio is a measure of the amount ofoscillation; the closed loop response time is a measure of the speed ofresponse; and an overshoot indicator is used to signify the presence ofovershoot. Rules and equations are needed to determine the features forboth load disturbances and setpoint changes and for different types ofresponses, e.g., underdamped, overdamped, and unstable response types.Next, a method for determining key signals and features following asetpoint change and load disturbance is described. Then, the procedurefor generating the oscillation ratio, closed loop response time, andovershoot indicator from the key signals is described.

An exemplary method for extracting key signals and features followingsetpoint change at block 107 will now be described with reference toFIGS. 4A-4C The method begins with a search for the low 201 and high 203control signals (u_(low) and u_(high)). The search is initiated at thetime of the setpoint change 205. The initial values for the low and highvalues of the smoothed process output (ŷ_(low), ŷ_(high)) are set equalto the old setpoint (y_(set,old)) Also, the associated times({circumflex over (t)}_(ylow), {circumflex over (t)}_(yhigh)) are setequal to the time of the setpoint change. The method continues searchingfor signals (ŷ_(low), ŷ_(high), t_({overscore (y)}low),t_({overscore (y)}high)) after the smoothed process output exceeds anupper threshold equal to the smoothed process output at the time of thesetpoint change plus the tune band. Also, the search for low and highvalues of the slope (ŝ_(low), ŝ_(high), t_(ŝ) _(low) , t_(ŝ) _(high) )and sign changes in the slope begins after the smoothed process outputexceeds the upper threshold. The method stops searching for signalsafter the slope changes sign two times, or the time since the setpointchange equals 60 times the sampling period for the PI controller,whichever occurs first.

Due to limited resolution of the analog to digital converter, the slopeestimate may equal zero for a number of samples. It is important todetect a single sign change when the slope goes from a positive value tozero, and then from zero to a negative value. The following rule is usedto identify a sign change in the slope:IF (ŝ _(t-1) ŝ _(t)<0) OR (ŝ _(t-1)≠0 and ŝ _(t)=0) THEN sign change inslope  (11)where ŝ_(t) and ŝ_(t-1) are the current and previous estimate of theslope, respectively.

If the search for the signals and sign changes in the slope would beginbefore the smoothed process output exceeded the upper threshold, thenthe first sign change would be incorrectly identified and theoscillation ratio would have a wrong value. Thus, to prevent incorrectdetermination of features after an increase in setpoint, the search forsignals from the process output and slope should begin after thesmoothed process output exceeds an upper threshold. Following a decreasein setpoint, the search for signals begins after the smoothed processoutput is less than a lower threshold equal to the smoothed processoutput at the time of the setpoint change minus the tune band. The sevenfeatures are determined when the second sign change is identified, orthe time since the setpoint change equals 60 T.

Referring now to FIGS. 5A-5C, an exemplary method will be described forsearching for load disturbances when not determining features for alarge setpoint change. A load disturbance is considered large when twoconsecutive samples of the smoothed process output are greater than thesetpoint plus tune band, or two consecutive samples are less than thesetpoint minus the tune band. At the time of the load disturbance, themethod begins searching for low and high control signals and signchanges in the slope. After the first sign change is identified, thesearch for the following signals begins:ŷ_(low),ŷ_(high),t_(ŷlow),t_(ŷhigh),ŝ_(low),ŝ_(high),t_(ŝ) _(low) ,t_(ŝ)_(high) . The search for these signals stops after the slope changessign three times, or the time since the load disturbance change is 75times the sampling period, whichever occurs first.

If the slope does not change sign and the time since the loaddisturbance change is 75 times the sampling period, then the followingequations are used to determine the low and high values for the smoothedprocess output:ŷ _(low)=min{ŷ _(t) _(L) ₊₇₄ , ŷ _(t) _(L) ₊₇₅}  (12)ŷ _(high)=max{ŷ _(t) _(L) ₊₇₄ , ŷ _(t) _(L) ₊₇₅}  (13)where ŷ_(t) _(L) ₊₇₄ and ŷ_(t) _(L) ₊₇₅, are the smoothed process output74 and 75 time periods after the load disturbance was identified,respectively. The indication of overshoot, oscillation ratio (φ), andclosed loop response time (θ) are determined from the setpoint (y_(set))and the following signals: ŷ_(low), ŷ_(high), t_(ŷlow), t_(ŷ) _(high),ŝ_(low), ŝ_(high), t_(ŝ) _(low) , and t_(ŝ) _(high) .

Overshoot is determined by comparing the low and high values for thesmoothed process output with the setpoint (y_(set)). There is noovershoot if the low value is greater than the setpoint(ŷ_(low)>y_(set)), or if the high value is less than the setpoint(ŷ_(high)<y_(set)).

The oscillation ratio is set to zero if any of the conditions in Table 1(shown below) are satisfied. The first three conditions are designed todetect a sluggish closed loop response for a low noise system. Thefourth condition is designed to detect a sluggish response for a systemwith a large amount of noise. The last three conditions are designed todetect a sluggish response for systems that have periodic loaddisturbances.

TABLE 1 Conditions where oscillation ratio equals zero. Condition NumberCondition Comment 1 ŝ_(low) and ŝ_(high) not determined Sluggishresponse 2 ŝ_(low) ≧ 0 Sluggish response 3 ŝ_(high) ≦ 0 Sluggishresponse 4 No overshoot & (ŷ_(high) − ŷ_(low)) > 2T_(band) Sluggishresponse & noisy 5${{{{{{{No}\quad{overshoot}}\quad\&}\quad t_{{\hat{y}}_{high}}} > t_{{\hat{y}}_{low}}}\quad\&}\quad{\frac{{\hat{y}}_{high} - y_{set}}{{\hat{y}}_{low} - y_{set}}}} > 0.5$Sluggish & periodic disturbance 6${{{{{{{No}\quad{overshoot}}\quad\&}\quad t_{{\hat{y}}_{low}}} > t_{{\hat{y}}_{high}}}\quad\&}\quad{\frac{{\hat{y}}_{low} - y_{set}}{{\hat{y}}_{high} - y_{set}}}} > 0.5$Sluggish & periodic disturbance 7 No overshoot for three consecutiveSluggish & periodic patterns disturbance

If none of the conditions in Table 1 are satisfied, then the oscillationratio and closed loop response time are determined from: $\begin{matrix}{\phi = \left\{ \begin{matrix}{\min\quad\left( {1,{\frac{{\hat{s}}_{low}}{{\hat{s}}_{high}}}} \right)} & {{{when}\quad t_{{\hat{s}}_{low}}} > t_{{\hat{s}}_{high}}} \\{\min\quad\left( {1,{\frac{{\hat{s}}_{high}}{{\hat{s}}_{low}}}} \right)} & {{{when}\quad t_{{\hat{s}}_{high}}} > t_{{\hat{s}}_{low}}}\end{matrix} \right.} & (14) \\{\theta = \frac{{\hat{y}}_{high} - {\hat{y}}_{low}}{\max\quad\left( {{{\hat{s}}_{low}},{{\hat{s}}_{high}}} \right)}} & (15)\end{matrix}$where φ is the oscillation ratio, θ is the closed loop response time,ŝ_(low) and ŝ_(high) are the low and high estimates of the slope,respectively, and ŷ_(low) and ŷ_(high) are the low and high estimates ofthe process output, respectively.

After block 107 characterizes the closed loop response, block 109determines if there is an insignificant pattern or if the controlleroutput is saturated. To prevent the control system from detuning, thecontroller parameters are not updated when there is an insignificantpattern. Block 109 considers a pattern insignificant when the controlleroutput makes a small change and the smoothed process output goes betweenthe setpoint plus tune band and the setpoint minus tune band at leastonce during the time that the pattern is being characterized. The changein controller output is determined by subtracting the low controlleroutput from the high controller output. A change in controller output isconsidered small (or insignificant) if it involves a move of less thanfive percent of the range of the controller output. The range of thecontroller output is equal to the upper limit for the control signalminus the lower limit for the control signal. Thus, the following ruleis used to determine if pattern is considered insignificant:

 IF ŷ _(high)≧(y _(set) −T _(band)) and ŷ _(low)≦(y _(set) +T _(band))and (u _(high) −u _(low))<0.05(u _(max) −u _(min)) THEN pattern isinsignificant   (16)

As indicated previously, the output of the PI controller can becomesaturated (e.g., have a continuous output of 0% or 100%) after a largedisturbance in which the load exceeds the range of the process. Also,the controller output can saturate if system 100 does not have knowledgethat the PI controller is in a manual mode of operation. If thecontroller output saturates and the load cannot be met, then the gainand integral time of the PI controller are not updated because thecontroller is doing all it can to move the process output towardssetpoint. The strategy of not updating the controller parameters whenthe controller output saturates is analogous to anti-reset wind-upstrategies for controllers with integral action.

Block 109 uses the following procedure to detect a saturated controlleroutput. First, the low and high signals for the controller output arerecorded during the time period that the patterns are beingcharacterized. Second, the following rule is used to determine if thecontroller output is considered saturated:IF Ke>0 and u_(low)>(0.05u _(min)+0.95u _(max)) OR Ke<0 and u_(high)<(0.95u _(min)+0.05u _(max)) THEN controller output issaturated  (17)where K is the controller gain and e is the error.

The following rule in block 109 prevents system 100 from slowly detuningundersized capacity systems that are frequently restarted: Set u_(high)and u_(low) in rule (17) equal to the last output of the controller forthe first update of the gain and integral time following the initialstart of system 100, or a restart of system 100. If the controlleroutput is saturated, the gain and integral time value remain the same.Otherwise, system 100 advances to block 111.

At block 111, system 100 determines a new gain and integral time. Thenew value for the controller gain is determined from:K _(new) =K+λ _(sn)λ_(ss)({circumflex over (K)} _(opt) −K)   (18)where K_(new) is the new value for the controller gain, K is the presentvalue for the controller gain, {circumflex over (K)}_(opt) is theestimate of optimal gain, λ_(sn) is a smoothing constant based on thesize of the present pattern relative to the tune band, and λ_(ss) is thesmoothing constant based on the size of the present pattern relative tothe size of previous patterns.

The optimal gain is determined in block 111 from the oscillation ratio(φ) using:{circumflex over (K)} _(opt)=K(1.2922−2.4940φ+4.3174φ²−4.4524φ³+1.8233φ⁴)  (19)The smoothing constant for the signal to noise ratio is determined from:$\begin{matrix}{\lambda_{sn} = {\min\quad\left\{ {{\max\quad\left\{ {0,\left( \frac{\frac{\sigma}{T_{band}} - 0.95}{0.32} \right)} \right\}},1} \right\}}} & (20)\end{matrix}$where σ is the signal size for the present pattern. The smoothingconstant for the signal size ratio is one the first four times thatblock 111 is used to update gain and integral time. This allows system100 to quickly adjust the controller parameters when it is first startedup. If block 111 has updated the controller parameters five or moretimes, then the smoothing constant for the signal size is determinedfrom: $\begin{matrix}{\lambda_{ss} = {\min\quad\left\{ {{\max\quad\left\{ {0,\left( \frac{\sigma}{2.19\quad\overset{\_}{\sigma}} \right)} \right\}},1} \right\}}} & (21)\end{matrix}$where {overscore (σ)} is an exponentially weighted moving average ofpast signal sizes.

If there has been overshoot (i.e., smoothed process output has crossedsetpoint) in any of the last three patterns, then the signal size isdetermined from:σ=max{ŷ _(high) , y _(set)}−min{ŷ _(low) , y _(set)}  (22)If there has been no overshoot in the last three patterns, then thesignal size is determined from:σ=max{{overscore (σ)}, T _(band), (max{ŷ _(high) , y _(set)}−min{ŷ_(low) , y _(set)})}  (23)

The average signal size is determined from:{overscore (σ)}_(p)={overscore (σ)}_(p-1)+λ(σ_(p)−{overscore(σ)}_(p-1))  (24)where {overscore (σ)}_(p) is the average signal size based on ppatterns, λ is an exponential smoothing constant that was set to 0.05,and σ_(p) is determined from equation (22) for pattern p. For the first1/λ patterns, the average signal size is determined from:$\begin{matrix}{{\overset{\_}{\sigma}}_{p} = {{\overset{\_}{\sigma}}_{p - 1} + {\frac{1}{p}\left( {\sigma_{p} - {\overset{\_}{\sigma}}_{p - 1}} \right)}}} & (25)\end{matrix}$where p is a running index for the number of patterns that have beencharacterized.

When the oscillation ratio is greater than 0.02, then the response isnot sluggish and a new value for the integral time is determined from:

 T _(i,new) =T _(i)+λ_(sn)λ_(ss)({circumflex over (T)} _(i,opt) −T_(i))  (26)where{circumflex over (T)} _(i,opt) =T min{30, max{2,(−3.429+1.285θ)}}  (27)where λ_(sn) and λ_(ss) are determined from equations (20) and (21),respectively.

It is important to note that the above-described preferred embodimentsare illustrative only. Although the invention has been described inconjunction with specific embodiments thereof, those skilled in the artwill appreciate that numerous modifications are possible withoutmaterially departing from the novel teachings and advantages of thesubject matter described herein. Accordingly, these and all other suchmodifications are intended to be included within the scope of thepresent invention as defined in the appended claims. The order orsequence of any process or method steps may be varied or re-sequencedaccording to alternative embodiments. In the claims, anymeans-plus-function clause is intended to cover the structures describedherein as performing the recited function and not only structuralequivalents but also equivalent structures. Other substitutions,modifications, changes and omissions may be made in the design,operating conditions and arrangement of the preferred and otherexemplary embodiments without departing from the spirit of the presentinvention.

1. A method of dynamically adjusting the control parameters of aproportional gain and integral time controller disposed to control anactuator affecting a process, comprising: sampling a feedback signalrepresentative of a controlled variable of the process to generate asampled signal; generating a smoothed signal based on the sampledsignal; determining an estimated noise level of the sampled signal;determining if control output and process output are oscillating quicklybased on predefined criteria; adjusting the gain used by the controllerif the control output and process output are oscillating quickly; andutilizing the adjusted control parameters to control the actuator,thereby causing the proportional gain and integral time controller toaffect the process.
 2. The method of claim 1, wherein adjusting the gaincomprises decreasing the gain.
 3. The method of claim 1, furthercomprising determining whether a significant load disturbance occurredby comparing a tune noise band to the difference between a currentsetpoint value and the smoothed signal if the control output and processoutput are not oscillating quickly.
 4. The method of claim 3, whereinthe gain and integral time remain the same if a significant loaddisturbance has not occurred.
 5. The method of claim 3, furthercomprising characterizing a closed loop response by monitoring featuresfrom a control signal, smoothed process output, and slope of the processoutput if a significant load disturbance occurred.
 6. The method ofclaim 5, further comprising determining whether a pattern isinsignificant based on a setpoint and tune noise band or whether thecontrol output is saturated.
 7. The method of claim 6, wherein the gainand integral time remain the same when the pattern is insignificant orthe control output is saturated.
 8. The method of claim 6, wherein a newgain and new integral time are determined when the pattern is notinsignificant and the control output is not saturated.
 9. The method ofclaim 8, wherein the new gain and new integral time are determined basedon estimated optimal gain, estimated optimal integral time, current gainand integral time values used in the proportional gain and integral timecontroller, a signal-to-noise ratio of the sampled signal, and size of acurrent load disturbance or setpoint change relative to averagedisturbance size.
 10. An apparatus for dynamically adjusting controlparameters of a proportional gain and integral time controller disposedto control an actuator affecting a process, comprising: means forsampling a feedback signal representative of a controlled variable ofthe process to generate a sampled signal; means for generating asmoothed signal based on the sampled signal; means for determining anestimated noise level of the sampled signal; means for determining ifcontrol output and process output are oscillating quickly based onpredefined criteria; means for adjusting the gain used by the controllerif the control output and process output are oscillating quickly; andmeans for utilizing the adjusted control parameters to control theactuator, thereby causing the proportional gain and integral timecontroller to affect the process.
 11. The apparatus of claim 10, whereinadjusting the gain comprises decreasing the gain.
 12. The apparatus ofclaim 10, further comprising means for determining whether a significantload disturbance occurred by comparing a tune noise band to thedifference between a current setpoint value and the smoothed signal ifthe control output and process output are not oscillating quickly. 13.The apparatus of claim 12, further comprising means for not changing thegain and integral time values if a significant load disturbance has notoccurred.
 14. The apparatus of claim 12, further comprising means forcharacterizing a closed loop response by monitoring features from acontrol signal, smoothed process output, and slope of the process outputif a significant load disturbance occurred.
 15. The apparatus of claim14, further comprising means for determining whether a pattern isinsignificant based on a setpoint and tune noise band or whether thecontrol output is saturated.
 16. The apparatus of claim 15, furthercomprising means for not changing the gain and integral time values whenthe pattern is insignificant or the control output is saturated.
 17. Theapparatus of claim 15, further comprising means for determining a newgain and new integral time when the pattern is not insignificant and thecontrol output is not saturated.
 18. The apparatus of claim 16, furthercomprising a means for determining a new gain and new integral timebased on an estimated optimal gain, estimated optimal integral time,current gain and integral time values used in the proportional gain andintegral time controller, a signal-to-noise ratio of the sampled signal,and size of a current load disturbance or setpoint change relative toaverage disturbance size.
 19. A method of dynamically adjusting thecontrol parameters of a proportional gain and integral time controllerdisposed to control an actuator affecting a process, comprising:sampling a feedback signal representative of a controlled variable ofthe process to generate a sampled signal; generating a smoothed signalbased on the sampled signal; determining an estimated noise level of thesampled signal; determining whether a pattern is insignificant based ona setpoint and tune noise band and whether the control output issaturated; determining a new gain and a new integral time and settingthe gain and integral time of the proportional gain and integral timecontroller to the new gain and new integral time if the pattern is notinsignificant and the control output is not saturated; and utilizing theadjusted control parameters to control the actuator, thereby causing theproportional gain and integral time controller to affect the process.20. The method of claim 19, wherein the new gain and new integral timeare determined based on an estimated optimal gain, estimated optimalintegral time, current gain and integral time values used in theproportional gain and integral time controller, a signal-to-noise ratioof the sampled signal, and size of a current load disturbance orsetpoint change relative to average disturbance size.
 21. The method ofclaim 19, further comprising determining whether control output andprocess output are oscillating quickly based on predefined criteria. 22.The method of claim 21, further comprising adjusting the gain used bythe proportional gain and integral time controller if the control outputand process output are oscillating quickly, wherein adjusting the gaincomprises decreasing the gain.
 23. The method of claim 21, furthercomprising determining whether a significant load disturbance hasoccurred by comparing a tune noise band to the difference between acurrent setpoint value and the smoothed signal if the control output andprocess output are not oscillating quickly.
 24. The method of claim 23,wherein the gain and integral time remain the same if a significant loaddisturbance has not occurred.
 25. The method of claim 24, furthercomprising characterizing a closed loop response by monitoring featuresfrom a control signal, smoothed process output, and slope of the processoutput if a significant load disturbance has occurred.
 26. The method ofclaim 19, wherein the gain and integral time remain the same when thepattern is insignificant or the control output is saturated.